[MNT] add test download

tests/test_client/test_download.py

@@ -0,0 +1,33 @@
+import os
+import numpy as np
+
+import learnware
+from learnware.client import LearnwareClient
+from learnware.client.container import ModelEnvContainer, LearnwaresContainer
+from learnware.learnware.reuse import AveragingReuser
+
+
+if __name__ == "__main__":
+    email = "liujd@lamda.nju.edu.cn"
+    token = "f7e647146a314c6e8b4e2e1079c4bca4"
+    
+    client = LearnwareClient()
+    client.login(email, token)
+    
+    learnware_ids = ["00000084", "00000154", "00000155"]
+    zip_paths = ["1.zip", "2.zip", "3.zip"]
+    root = os.path.dirname(__file__)
+    for i in range(len(learnware_ids)):
+        zip_paths[i] = os.path.join(root, zip_paths[i])
+        client.download_learnware(learnware_ids[i], zip_paths[i])
+    
+    learnware_list = [client.load_learnware(file, load_model=False) for file in zip_paths]
+
+    with LearnwaresContainer(learnware_list, zip_paths) as env_container:
+        learnware_list = env_container.get_learnware_list_with_container()
+        reuser = AveragingReuser(learnware_list, mode="vote_by_label")
+        input_array = np.random.random(size=(20, 13))
+        print(reuser.predict(input_array))
+        
+        for idx, learnware in enumerate(learnware_list):
+            print(f"learnware_{idx}", reuser.predict(learnware))

tests/test_client/test_reuse.py

@@ -0,0 +1,42 @@
+import zipfile
+import numpy as np
+
+from learnware.learnware import get_learnware_from_dirpath, Learnware
+from learnware.market import EasyMarket
+from learnware.client.container import ModelEnvContainer, LearnwaresContainer
+from learnware.learnware.reuse import AveragingReuser
+
+if __name__ == "__main__":
+    semantic_specification = dict()
+    semantic_specification["Data"] = {"Type": "Class", "Values": ["Text"]}
+    semantic_specification["Task"] = {"Type": "Class", "Values": ["Ranking"]}
+    semantic_specification["Library"] = {"Type": "Class", "Values": ["Scikit-learn"]}
+    semantic_specification["Scenario"] = {"Type": "Tag", "Values": "Financial"}
+    semantic_specification["Name"] = {"Type": "String", "Values": "test"}
+    semantic_specification["Description"] = {"Type": "String", "Values": "test"}
+
+    zip_paths = [
+        "/home/bixd/workspace/learnware/Learnware/tests/test_learnware_client/rf_tic.zip",
+        "/home/bixd/workspace/learnware/Learnware/tests/test_learnware_client/svc_tic.zip",
+    ]
+    dir_paths = [
+        "/home/bixd/workspace/learnware/Learnware/tests/test_learnware_client/rf_tic",
+        "/home/bixd/workspace/learnware/Learnware/tests/test_learnware_client/svc_tic",
+    ]
+
+    learnware_list = []
+    for id, (zip_path, dir_path) in enumerate(zip(zip_paths, dir_paths)):
+        with zipfile.ZipFile(zip_path, "r") as z_file:
+            z_file.extractall(dir_path)
+
+        learnware = get_learnware_from_dirpath(f"test_id{id}", semantic_specification, dir_path)
+        learnware_list.append(learnware)
+
+    with LearnwaresContainer(learnware_list, zip_paths) as env_container:
+
+        learnware_list = env_container.get_learnware_list_with_container()
+        reuser = AveragingReuser(learnware_list, mode="vote")
+        input_array = np.random.randint(0, 3, size=(20, 9))
+        print(reuser.predict(input_array).argmax(axis=1))
+        for id, ind_learner in enumerate(learnware_list):
+            print(f"learner_{id}", reuser.predict(input_array).argmax(axis=1))